





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_A					:				int				=			get_tests_dir("""fixtures""")



class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			mock.Mock()
        SCREAMING_SNAKE_CASE__											=			5_00
        SCREAMING_SNAKE_CASE__											=			{}
        SCREAMING_SNAKE_CASE__											=			HTTPError
        SCREAMING_SNAKE_CASE__											=			{}

        # Download this model to make sure it's in the cache.
        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''     )
        # Under the mock environment we get a 500 error when trying to reach the model.
        with mock.patch('''requests.Session.request'''   ,				return_value=A_     ) as mock_head:
            SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''     )
            # This check we did call the fake head request
            mock_head.assert_called()




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained(
            '''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'''     )





@is_staging_test
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @classmethod
    def  lowercase_					(       cls     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TOKEN
        HfFolder.save_token(A_     )



    @classmethod
    def  lowercase_					(       cls     ):





        '''simple docstring'''

        try:
            delete_repo(token=cls._token   ,				repo_id='''test-feature-extractor'''     )
        except HTTPError:
            pass

        try:
            delete_repo(token=cls._token   ,				repo_id='''valid_org/test-feature-extractor-org'''     )
        except HTTPError:
            pass

        try:
            delete_repo(token=cls._token   ,				repo_id='''test-dynamic-feature-extractor'''     )
        except HTTPError:
            pass



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained(A_     )
        feature_extractor.push_to_hub('''test-feature-extractor'''   ,				use_auth_token=self._token     )

        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''     )
        for k, v in feature_extractor.__dict__.items():
            self.assertEqual(A_   ,				getattr(A_   ,				A_     )     )

        # Reset repo
        delete_repo(token=self._token   ,				repo_id='''test-feature-extractor'''     )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
            feature_extractor.save_pretrained(
                A_   ,				repo_id='''test-feature-extractor'''   ,				push_to_hub=A_   ,				use_auth_token=self._token     )

        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''     )
        for k, v in feature_extractor.__dict__.items():
            self.assertEqual(A_   ,				getattr(A_   ,				A_     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained(A_     )
        feature_extractor.push_to_hub('''valid_org/test-feature-extractor'''   ,				use_auth_token=self._token     )

        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor'''     )
        for k, v in feature_extractor.__dict__.items():
            self.assertEqual(A_   ,				getattr(A_   ,				A_     )     )

        # Reset repo
        delete_repo(token=self._token   ,				repo_id='''valid_org/test-feature-extractor'''     )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
            feature_extractor.save_pretrained(
                A_   ,				repo_id='''valid_org/test-feature-extractor-org'''   ,				push_to_hub=A_   ,				use_auth_token=self._token     )

        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor-org'''     )
        for k, v in feature_extractor.__dict__.items():
            self.assertEqual(A_   ,				getattr(A_   ,				A_     )     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        CustomFeatureExtractor.register_for_auto_class()
        SCREAMING_SNAKE_CASE__											=			CustomFeatureExtractor.from_pretrained(A_     )

        feature_extractor.push_to_hub('''test-dynamic-feature-extractor'''   ,				use_auth_token=self._token     )

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            feature_extractor.auto_map   ,				{'''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor'''}   ,				)

        SCREAMING_SNAKE_CASE__											=			AutoFeatureExtractor.from_pretrained(
            f'''{USER}/test-dynamic-feature-extractor'''   ,				trust_remote_code=A_     )
        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
        self.assertEqual(new_feature_extractor.__class__.__name__   ,				'''CustomFeatureExtractor'''     )


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

